Neural networks training for weapon selection in first-person shooter games

Authors:
Stelios Petrakis;Anastasios Tefas
Affiliations:
Department of Informatics, Aristotle University of Thessaloniki, Greece;Department of Informatics, Aristotle University of Thessaloniki, Greece
Venue:
ICANN'10 Proceedings of the 20th international conference on Artificial neural networks: Part III
Year:
2010

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Abstract

First person shooters is probably the most well known genre of the whole gaming industry. Bots in those games must think and act fast in order to be competitive and fun to play with. Key part of the action in a first person shooter is the choice of the right weapon according to the situation. In this paper, a weapon selection technique is introduced in order to produce competent agents in the first person shooter game Unreal Tournament 2004 utilizing the Pogamut 2 Game-Bots library. The use of feedforward neural networks is proposed, trained with back-propagation for weapon selection, showing that that there is a significant increase at the performance of a bot.